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1. A method for identifying protein-drug interactions, the method comprising: receiving test ligand molecular data corresponding to a test ligand that is a candidate drug; receiving protein molecular data corresponding to a protein; receiving reference ligand data corresponding to a reference ligand that binds to the protein; generating, by a computer system, an atom pair score from an atom descriptor of the test ligand and a corresponding atom descriptor of the reference ligand; calculating an interaction score, the interaction score including a sum comprising the atom pair score; normalizing the interaction score using a normalization weight calculated using the formula N ∝ =1−|1−S ∝ (x)|, wherein x is a raw parameter, S(x) is a sigmoid function, and a is a tunable scalar coefficient chosen to maximize an information-preserving variance in an image of N(x); validating, based on the interaction score exceeding a threshold value, a first interaction between the test ligand and the protein; in response to validating the first interaction, performing an assay using the test ligand and the protein to obtain an assay result experimentally; after validating the first interaction, receiving the assay result; and confirming the first interaction based at least on the received assay result.
2. The method of claim 1 , further comprising: calculating, by the computer system, a shape score including one or more shape contributions, each shape contribution corresponding to a respective shape descriptor, wherein a respective contribution includes a first part and a second part, the first part providing a first shape score from a first respective shape function of the protein and the test ligand corresponding to the respective shape descriptor, and the second part providing a second shape score from a second respective shape function of the reference ligand and the test ligand corresponding to the respective shape descriptor; calculating, by the computer system, a total similarity score including one or more similarity contributions, each similarity contribution corresponding to a respective similarity descriptor, wherein a respective similarity contribution provides a respective similarity score between a respective similarity function of the test ligand and the respective similarity function of the reference ligand; calculating, by the computer system, a correction score, the correction score being a difference between a first sum and a second sum, the first sum being of energies of contact points between the reference ligand and the protein; and adding the shape score, the similarity score, and the correction score to the interaction score.
3. The method of claim 2 , further comprising: calculating a docking score between the test ligand and the protein; and adding the docking score to the interaction score.
The invention relates to computational methods for evaluating interactions between a test ligand and a protein, particularly in drug discovery or molecular docking applications. The problem addressed is the need for accurate and efficient assessment of ligand-protein binding affinity to identify potential drug candidates. The method involves simulating the binding of a test ligand to a protein and calculating an interaction score based on the binding pose. Additionally, the method includes calculating a docking score that quantifies the stability or likelihood of the binding pose and incorporating this docking score into the overall interaction score. This combined score provides a more comprehensive evaluation of the ligand-protein interaction, improving the accuracy of predictions for drug development. The method may also involve generating multiple binding poses for the ligand and selecting the pose with the highest interaction score, ensuring optimal binding configurations are considered. The docking score may be derived from energy calculations, statistical models, or other computational techniques that assess the physical and chemical compatibility of the ligand with the protein binding site. By integrating the docking score with the interaction score, the method enhances the reliability of virtual screening and docking simulations, facilitating the identification of promising drug candidates.
4. The method of claim 3 , further comprising: normalizing the interaction score using weights for the atom pair score, the first respective shape functions, the second respective shape functions, and the docking score.
5. The method of claim 3 , wherein the interaction score Z for a qth test ligand, a pth protein, and a rth reference ligand is calculated using the formula: Z ( q , p , r ) = ω j Y ( p , q ) + ω k P ( r , q ) + ∑ m = 1 M [ ω m f m ( p , q ) + ω m ′ f m ′ ( r , qp , q ) ] + ∑ n = 1 N X n ( r , q ) + CS ( OLIC ) .
6. The method of claim 2 , wherein the one or more shape contributions include a Euclidean distance metric.
This invention relates to methods for analyzing and processing geometric shapes, particularly in applications requiring precise shape comparison or matching. The problem addressed is the need for accurate and computationally efficient techniques to quantify similarities or differences between shapes, which is critical in fields such as computer vision, pattern recognition, and medical imaging. The method involves generating one or more shape contributions, which are quantitative measures derived from the geometric properties of a shape. These contributions are used to assess how closely one shape resembles another. A key aspect of the invention is the inclusion of a Euclidean distance metric, which calculates the straight-line distance between corresponding points on the shapes being compared. This metric helps in determining spatial relationships and alignment, improving the accuracy of shape analysis. The method may also involve preprocessing steps, such as normalizing the shapes to a common scale or orientation, to ensure consistent comparisons. Additionally, the shape contributions can be combined or weighted to refine the analysis, allowing for more nuanced assessments of shape similarity. The use of Euclidean distance ensures that the method is both intuitive and mathematically robust, making it suitable for applications where precise geometric alignment is required. The invention provides a flexible and efficient approach to shape comparison, enhancing the reliability of shape-based decision-making in various technical domains.
7. The method of claim 2 , wherein the one or more similarity contributions include at least one of: a number of H-bond acceptors, number of H-bond donors, dipole, electron affinity, globularity, molecular weight, ClogP, number of rotatable bonds, solvent-accessible surface area, and volume.
8. The method of claim 2 , wherein: the first sum of the correction score for a pth protein is computed as: S(OLIC−r) p =Σ n=1 NR ω n E n,p , where NR is a number of contact points between the reference ligand and the protein, ω n is a weighting factor for the nth contact point, and E n,p is an energy associated with the nth contact point.
9. The method of claim 2 , wherein: the second sum of the correction score for a qth test ligand and a pth protein is computed as: S(OLIC−q) p =Σ n=1 NQ ω n E n,q,p , where NQ is a number of contact points between the test ligand and the protein, ω n is a weighting factor for the nth contact point, and E n,p,q is an energy associated with the nth contact point.
10. The method of claim 1 , wherein a portion of the reference ligand data is extracted from a known structure of a complex of the protein bound to the reference ligand.
11. The method of claim 1 , wherein calculating the atom pair score includes a similarity Tanimoto coefficient.
The invention relates to computational chemistry and cheminformatics, specifically to methods for comparing molecular structures to identify similarities or differences. The problem addressed is the need for efficient and accurate ways to assess molecular similarity, which is crucial for drug discovery, chemical research, and material science. Traditional methods often rely on comparing molecular fingerprints or structural features, but these can be computationally intensive or lack precision. The method involves calculating an atom pair score to quantify the similarity between two molecules. This score is derived by comparing pairs of atoms within each molecule, considering their chemical properties and spatial relationships. The key innovation is the use of a similarity Tanimoto coefficient in this calculation. The Tanimoto coefficient is a mathematical measure that evaluates the overlap between two sets, normalized to account for differences in set size. By applying this coefficient to atom pair comparisons, the method provides a more nuanced and accurate assessment of molecular similarity. This approach improves upon prior methods by reducing computational overhead while enhancing the precision of similarity measurements. The technique is particularly useful in large-scale screening applications where rapid and reliable molecular comparisons are essential.
12. A computer product comprising a non-transitory computer readable medium storing a plurality of instructions for controlling a computer system to perform the method of claim 1 .
13. A system comprising: the computer product of claim 12 ; and one or more processors for executing instructions stored on the computer readable medium.
14. The method of claim 1 , wherein the assay comprises a kinase assay.
A kinase assay method is disclosed for detecting or quantifying kinase activity in a sample. The method involves providing a sample containing a kinase enzyme, adding a substrate that the kinase can phosphorylate, and detecting the phosphorylation of the substrate to measure kinase activity. The assay may include a detection reagent that binds to the phosphorylated substrate, allowing for the measurement of kinase activity through a detectable signal. The method can be used to screen for kinase inhibitors or activators, study kinase function, or diagnose diseases associated with abnormal kinase activity. The assay may be performed in a multi-well plate format for high-throughput screening. The kinase assay can be adapted for various detection techniques, including fluorescence, luminescence, or colorimetric methods, depending on the detection reagent used. The method provides a sensitive and specific way to monitor kinase activity, which is useful in drug discovery, biochemical research, and clinical diagnostics.
15. The method of claim 1 , wherein: the test ligand is a first test ligand, and the method further comprises: calculating interaction scores for 39 additional test ligands in the same manner as for the first test ligand.
16. The method of claim 1 , wherein the threshold value is a score value.
A system and method for evaluating data quality in a database or data processing system addresses the challenge of accurately assessing the reliability and consistency of stored or processed data. The invention provides a mechanism to determine whether data meets predefined quality standards by comparing it against a threshold value, which is represented as a score value. This score value quantifies the quality of the data based on various metrics, such as accuracy, completeness, or consistency. The method involves analyzing the data to generate a quality score and then comparing this score to the threshold value to determine if the data is acceptable. If the score falls below the threshold, the system may flag the data for review, correction, or exclusion from further processing. The threshold value can be dynamically adjusted based on user preferences, system requirements, or historical data trends to ensure optimal data quality control. This approach helps maintain high data integrity, reduces errors in downstream applications, and improves decision-making processes that rely on accurate data. The invention is particularly useful in industries where data accuracy is critical, such as finance, healthcare, and manufacturing.
17. The method of claim 1 , further comprising: determining a plurality of interaction scores for a plurality of test ligands and protein combinations; ranking the plurality of interaction scores; and using a ranking as the threshold value.
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February 9, 2021
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